The Potential Value and Impact of Diagnostic Biomarkers for MAFLD Using Machine Learning Methods
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 500
- 试验地点
- 1
- 主要终点
- Area under cure
研究概览
简要总结
This is a case-control study that aims to build a predictive model for MAFLD based on machine learning.
详细描述
Metabolic dysfunction-associated fatty liver disease (MAFLD) also known as non-alcoholic fatty liver disease (NAFLD), is one of the most prevalent liver diseases worldwide with high prevalence and economic burden, which affects 25% of global adult population. Despite extensive research on understanding the inner pathophysiology of MAFLD, it still keep growing with no approval therapy. Therefore, preventive measures are particularly important in diagnosing MAFLD. So far the liver biopsy is still the gold standard for diagnosis of MAFLD, however considering the invasive process and potential risks, it still has low acceptance for asymptomatic patients, thus non-invasive methods are necessary for this reason.
The purpose of this study is to establish a prediction model to identify MAFLD patients, which can accurately predict whether the participants have MAFLD according to the relevant metabolic indicators of the participants, without the need for invasive examinations such as tissue biopsy.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •aged 18 to 75 years;
- •meeting the diagnostic criteria of MAFLD;
- •no other organic lesions were found in imaging examination;
- •willing and able to sign informed consent.
排除标准
- •significant drinking history (weekly alcohol consumption ≥ 140g for male, or weekly alcohol consumption ≥ 70g for female);
- •presence of evidence for having hepatic steatosis, viral hepatitis, history of hepatic cancer, drug-induced liver injury, liver cirrhosis and other liver and biliary tract diseases;
- •major organ malfunction, severe systemic illnesses, mental health issues, or inability to complete examination;
- •pregnant or pregnancy planning female;
- •missing of important clinical data.
结局指标
主要结局
Area under cure
时间窗: 2022-2024
Area under cure(AUC) was defined as the area enclosed by the coordinate axis under the receiver operating characteristic curve, with values ranging from 0.5 to 1.0. The closer the AUC is to 1.0, the higher the authenticity of the detection method; the closer to 0.5, the lower the authenticity of the detection method; when equal to 0.5, the authenticity is the lowest and has no application value.
次要结局
- Accuracy(2022-2024)
- Precision(2022-2024)